Izvestiya of Saratov University.

Chemistry. Biology. Ecology

ISSN 1816-9775 (Print)
ISSN 2541-8971 (Online)

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Identification of Oils from Samara Region Using Principal Component Analysis and Factor Discriminant Analysis

Lobachev А. L., Samara State Technical University
Fomina N. V., Samara State Technical University
Monakhova Yu. B., Saratov State University

Development of methods for identification of oils is of high priority in oil industry. The following parameters for 2963 oil samples from five oilfields in the Samara region were determined: density, fraction yield at 200 °C and 300 °C, the mass fraction of sulfur, hydrogen sulphide, methyl and ethyl mercaptan, the mass concentration of chloride salts, the saturated vapor pressure. The matrix of experi- mental data was analyzed using principal component analysis (PCA) and factorial discriminant analysis (FDA) methods. The models obtained are able to determine the oilfield of samples with prob- ability of almost 100%. Chemometric models have been proved by the independent test set validation, which showed the accuracy and stability of the models. The results of the analysis indicated the prospects of application of chemometric methods in the investigation of oil samples from Samara region and the developed approach can be used to discriminate oils from another regions. 


1. Вигдергауз М. С. Аналитическая химия нефти. Куйбышев : Куйбыш. гос. ун-т, 1990. 27 с. 

2. Семенов В. А. Экоаналитическая идентификация источников загрязнений нефтяными углеводородами //
Разведка и охрана недр. 2005. № 5. С. 57–61. 

3. Cordella С. B. Y., Bertrand D. SAISIR : A new general chemometric toolbox // Trends Anal. Chem. 2014. Vol. 54. P. 75–82. 

4. Wold S., Esbensen K., Geladi P. Principal component
analysis // Chemom. Intell. Lab. Syst. 1987. Vol. 2. P. 37–52. 

5. Benzecri J. P. Analyse Discriminante et Analyse Factorielle // Les Cahiers de l’Analyse des Donnees. 1977. Vol. 2. P. 369–406. 

6. Родионова О. Е., Померанцев А. Л. Хемометрика: до-
стижения и перспективы // Успехи химии. 2006. Т. 75, № 4. С. 302–321. 

7. Monakhova Y. B., Kuballa T., Leitz J., Andlauer C., Lachenmeier D. W. NMR spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free
milk, and milk substitutes based on soy and grains // Dairy Sci. Technol. 2012. Vol. 92. P. 109–120. 

8. Macnaughtan Jr. D., Rogers L. B., Wernimont G. Principalcomponent analysis applied to chromatographic
data // Anal. Chem. 1972. Vol. 44. P. 1421–1427. 

9. Gergen I., Harmanescu M. Application of principal component analysis in the pollution assessment with heavy metals of vegetable food chain in the old mining areas //
Chem. Central J. 2012. Vol. 6. P. 156–162. 

10. Mouly P. P., Arzouyan C. R., Gaydou E. M., Estienne J. M. Differentiation of citrus juices by factorial discriminant analysis using liquid chromatography of flavanone
glycosides // J. Agric. Food Chem. 1994. Vol. 42. P. 70–79. 

11. Hammamia M., Rouissia H., Salaha N., Selmia H., Al-Otaibib M., Bleckerc C., Karoui R. Fluorescence spectroscopy coupled with factorial discriminant
analysis technique to identify sheep milk from different feeding // Food Chem. 2010. Vol. 122. P. 1344– 1350.